Is there really one AI economy?

Over the past few weeks I've found myself circling around the same question from several different directions. One day I was reading Cory Doctorow's argument that AI is becoming one of the largest speculative bubbles in history. A few days later Azeem Azhar argued almost the opposite: that increasingly capable open models could strengthen the business case for the companies building AI infrastructure rather than weaken it. Around the same time I was having much more practical conversations about AI subscriptions, APIs and why intelligence itself seems to be getting cheaper while many AI services still feel expensive.

None of those discussions seemed directly connected. Yet the longer I sat with them, the more I wondered whether they were all describing different parts of the same story.

This isn't an attempt to predict stock prices or tell anyone where to invest. Others are much better equipped to do that. What interests me is something slightly different. AI is increasingly discussed in terms of winners and losers, bubbles and breakthroughs, but I suspect we're still struggling to describe what we're actually looking at. Before deciding who will profit, it helps to understand what kind of economy is emerging in the first place.

Looking at different layers

One phrase I've started to question is AI company. We use it almost without thinking, but it quickly becomes slippery.

Nvidia is an AI company. So is OpenAI. Anthropic is one. Microsoft increasingly presents itself as one. Yet they are fundamentally different businesses. One designs chips, another trains frontier models, another rents out vast amounts of compute, while countless others simply build useful products on top of those foundations. Schmuki is also part of that wider AI economy, but in a completely different sense. We don't train models or build data centres. We help organisations understand and apply them.

The more I think about it, the less useful the label becomes. It compresses very different economic realities into a single category and, once we've done that, it becomes surprisingly easy to talk past one another.

A familiar historical pattern

The same applies to the bubble discussion. History suggests that speculative investment and genuine technological transformation are not opposites. They often arrive together.

Railways attracted extraordinary amounts of capital long before the network reached maturity. Electricity required decades of investment before it reshaped industry and everyday life. The commercial internet experienced its own boom and crash, yet few would argue today that it was ultimately overestimated. Investors can lose money while society gains an entirely new infrastructure.

That doesn't prove AI will follow the same path. History never repeats itself that neatly. But it does suggest that asking whether something is "a bubble" may be too narrow a lens through which to view a technology that could take decades to unfold.

Where does the value go?

One idea I keep returning to is that we often assume value has to stay in one place. If model providers face increasing competition and prices come down, someone must be losing.

Perhaps. But another possibility is that value simply moves.

Cheaper models make new applications viable. New applications create new demand. More demand requires more inference, more compute, more networking, more storage and, ultimately, more electricity. Pressure on one layer of the market can strengthen another. At the same time entirely new products, services and businesses become possible, just as previous waves of infrastructure created opportunities that were difficult to imagine beforehand.

The AI economy may not simply become more efficient. It may become larger.

Questions rather than conclusions

There is one aspect that does seem different to me. The early internet was remarkably open. Starting a website or running a server required relatively modest resources. Frontier AI currently appears much more concentrated. Training the largest models demands enormous amounts of capital, specialised hardware and access to energy. Whether that concentration proves temporary or becomes a defining feature of the coming decades is one of the questions I find most interesting.

Open-weight models point in one direction. The economics of scale point in another. I don't think anyone genuinely knows where the balance will settle.

Perhaps that's why I've become less interested in declaring AI either a bubble or a revolution. It may turn out to be both, in different ways and at different layers.

What I find more useful is stepping back from the daily headlines and trying to draw a better map. Not because it allows us to predict the future, but because it reminds us that we're looking at a landscape that is still taking shape. We know enough to recognise that AI is becoming a new layer of infrastructure. We know far less about where the long-term value will settle, which architectures will endure, or how today's concentration of power will evolve.

For now, that uncertainty doesn't feel like a weakness. It feels like an honest place to begin the conversation.

Related reading, if the AI economy piece resonated with you:

Compute Is the Entry Ticket, Not the Game
This one picks up directly where the "different layers" argument leaves off. If the AI economy piece asks what kind of businesses actually make up "AI," this piece follows one layer specifically, compute, and traces the chain from raw infrastructure through energy to real demand. It's the natural next step for the reader wondering "okay, but which layer actually captures the value?"

A Serious European Bet on AI Compute
The AI economy piece flags capital concentration as the one place where this wave looks genuinely different from the open early internet. This article grounds that abstract worry in a concrete case, Rotterdam's proposed AI campus, and pushes on the distinction between where infrastructure sits and who actually controls it. Good for readers who want the concentration question made tangible rather than theoretical.

Quantum Computing Is More Than Qubits
Less a direct continuation, more a sibling piece. The same instinct, resisting a tidy narrative about an emerging technology before we've actually understood what's being built, gets applied here to a different infrastructure story entirely. Useful if what drew you in was the "we're still mapping this, not predicting it" stance rather than AI specifically.